(mteb)= # MTEB 本框架支持 [MTEB](https://github.com/embeddings-benchmark/mteb) 和 [CMTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md)，具体介绍如下： - MTEB（Massive Text Embedding Benchmark）是一个大规模的基准测试，旨在衡量文本嵌入模型在多样化嵌入任务上的性能。MTEB 包括56个数据集，涵盖8个任务，并且支持超过112种不同的语言。这个基准测试的目标是帮助开发者找到适用于多种任务的最佳文本嵌入模型。 - CMTEB（Chinese Massive Text Embedding Benchmark）是一个专门针对中文文本向量的评测基准，它基于MTEB构建，旨在评测中文文本向量模型的性能。CMTEB收集了35个公共数据集，并分为6类评测任务，包括检索（retrieval）、重排序（reranking）、语义文本相似度（STS）、分类（classification）、对分类（pair classification）和聚类（clustering）。 ## 支持的数据集 | 名称 | Hub链接 | 描述 | 类型 | 类别 | 测试样本数量 | |-----|-----|---------------------------|-----|-----|-----| | [T2Retrieval](https://arxiv.org/abs/2304.03679) | [C-MTEB/T2Retrieval](https://modelscope.cn/datasets/C-MTEB/T2Retrieval) | T2Ranking：一个大规模的中文段落排序基准 | 检索 | s2p | 24,832 | | [MMarcoRetrieval](https://github.com/unicamp-dl/mMARCO) | [C-MTEB/MMarcoRetrieval](https://modelscope.cn/datasets/C-MTEB/MMarcoRetrieval) | mMARCO是MS MARCO段落排序数据集的多语言版本 | 检索 | s2p | 7,437 | | [DuRetrieval](https://aclanthology.org/2022.emnlp-main.357.pdf) | [C-MTEB/DuRetrieval](https://modelscope.cn/datasets/C-MTEB/DuRetrieval) | 一个大规模的中文网页搜索引擎段落检索基准 | 检索 | s2p | 4,000 | | [CovidRetrieval](https://aclanthology.org/2022.emnlp-main.357.pdf) | [C-MTEB/CovidRetrieval](https://modelscope.cn/datasets/C-MTEB/CovidRetrieval) | COVID-19新闻文章 | 检索 | s2p | 949 | | [CmedqaRetrieval](https://aclanthology.org/2022.emnlp-main.357.pdf) | [C-MTEB/CmedqaRetrieval](https://modelscope.cn/datasets/C-MTEB/CmedqaRetrieval) | 在线医疗咨询文本 | 检索 | s2p | 3,999 | | [EcomRetrieval](https://arxiv.org/abs/2203.03367) | [C-MTEB/EcomRetrieval](https://modelscope.cn/datasets/C-MTEB/EcomRetrieval) | 从阿里巴巴电商领域搜索引擎系统收集的段落检索数据集 | 检索 | s2p | 1,000 | | [MedicalRetrieval](https://arxiv.org/abs/2203.03367) | [C-MTEB/MedicalRetrieval](https://modelscope.cn/datasets/C-MTEB/MedicalRetrieval) | 从阿里巴巴医疗领域搜索引擎系统收集的段落检索数据集 | 检索 | s2p | 1,000 | | [VideoRetrieval](https://arxiv.org/abs/2203.03367) | [C-MTEB/VideoRetrieval](https://modelscope.cn/datasets/C-MTEB/VideoRetrieval) | 从阿里巴巴视频领域搜索引擎系统收集的段落检索数据集 | 检索 | s2p | 1,000 | | [T2Reranking](https://arxiv.org/abs/2304.03679) | [C-MTEB/T2Reranking](https://modelscope.cn/datasets/C-MTEB/T2Reranking) | T2Ranking：一个大规模的中文段落排序基准 | 重新排序 | s2p | 24,382 | | [MMarcoReranking](https://github.com/unicamp-dl/mMARCO) | [C-MTEB/MMarco-reranking](https://modelscope.cn/datasets/C-MTEB/Mmarco-reranking) | mMARCO是MS MARCO段落排序数据集的多语言版本 | 重新排序 | s2p | 7,437 | | [CMedQAv1](https://github.com/zhangsheng93/cMedQA) | [C-MTEB/CMedQAv1-reranking](https://modelscope.cn/datasets/C-MTEB/CMedQAv1-reranking) | 中文社区医疗问答 | 重新排序 | s2p | 2,000 | | [CMedQAv2](https://github.com/zhangsheng93/cMedQA2) | [C-MTEB/CMedQAv2-reranking](https://modelscope.cn/datasets/C-MTEB/C-MTEB/CMedQAv2-reranking) | 中文社区医疗问答 | 重新排序 | s2p | 4,000 | | [Ocnli](https://arxiv.org/abs/2010.05444) | [C-MTEB/OCNLI](https://modelscope.cn/datasets/C-MTEB/OCNLI) | 原始中文自然语言推理数据集 | 配对分类 | s2s | 3,000 | | [Cmnli](https://modelscope.cn/datasets/clue/viewer/cmnli) | [C-MTEB/CMNLI](https://modelscope.cn/datasets/C-MTEB/CMNLI) | 中文多类别自然语言推理 | 配对分类 | s2s | 139,000 | | [CLSClusteringS2S](https://arxiv.org/abs/2209.05034) | [C-MTEB/CLSClusteringS2S](https://modelscope.cn/datasets/C-MTEB/C-MTEB/CLSClusteringS2S) | 从CLS数据集中聚类标题。基于主要类别的13个集合的聚类。 | 聚类 | s2s | 10,000 | | [CLSClusteringP2P](https://arxiv.org/abs/2209.05034) | [C-MTEB/CLSClusteringP2P](https://modelscope.cn/datasets/C-MTEB/CLSClusteringP2P) | 从CLS数据集中聚类标题+摘要。基于主要类别的13个集合的聚类。 | 聚类 | p2p | 10,000 | | [ThuNewsClusteringS2S](http://thuctc.thunlp.org/) | [C-MTEB/ThuNewsClusteringS2S](https://modelscope.cn/datasets/C-MTEB/ThuNewsClusteringS2S) | 从THUCNews数据集中聚类标题 | 聚类 | s2s | 10,000 | | [ThuNewsClusteringP2P](http://thuctc.thunlp.org/) | [C-MTEB/ThuNewsClusteringP2P](https://modelscope.cn/datasets/C-MTEB/ThuNewsClusteringP2P) | 从THUCNews数据集中聚类标题+摘要 | 聚类 | p2p | 10,000 | | [ATEC](https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC) | [C-MTEB/ATEC](https://modelscope.cn/datasets/C-MTEB/ATEC) | ATEC NLP句子对相似性竞赛 | STS | s2s | 20,000 | | [BQ](https://huggingface.co/datasets/shibing624/nli_zh) | [C-MTEB/BQ](https://modelscope.cn/datasets/C-MTEB/BQ) | 银行问题语义相似性 | STS | s2s | 10,000 | | [LCQMC](https://huggingface.co/datasets/shibing624/nli_zh) | [C-MTEB/LCQMC](https://modelscope.cn/datasets/C-MTEB/LCQMC) | 大规模中文问题匹配语料库 | STS | s2s | 12,500 | | [PAWSX](https://arxiv.org/pdf/1908.11828.pdf) | [C-MTEB/PAWSX](https://modelscope.cn/datasets/C-MTEB/PAWSX) | 翻译的PAWS评测对 | STS | s2s | 2,000 | | [STSB](https://github.com/pluto-junzeng/CNSD) | [C-MTEB/STSB](https://modelscope.cn/datasets/C-MTEB/STSB) | 将STS-B翻译成中文 | STS | s2s | 1,360 | | [AFQMC](https://github.com/CLUEbenchmark/CLUE) | [C-MTEB/AFQMC](https://modelscope.cn/datasets/C-MTEB/AFQMC) | 蚂蚁金服问答匹配语料库 | STS | s2s | 3,861 | | [QBQTC](https://github.com/CLUEbenchmark/QBQTC) | [C-MTEB/QBQTC](https://modelscope.cn/datasets/C-MTEB/QBQTC) | QQ浏览器查询标题语料库 | STS | s2s | 5,000 | | [TNews](https://github.com/CLUEbenchmark/CLUE) | [C-MTEB/TNews-classification](https://modelscope.cn/datasets/C-MTEB/TNews-classification) | 新闻短文本分类 | 分类 | s2s | 10,000 | | [IFlyTek](https://github.com/CLUEbenchmark/CLUE) | [C-MTEB/IFlyTek-classification](https://modelscope.cn/datasets/C-MTEB/IFlyTek-classification) | 应用描述的长文本分类 | 分类 | s2s | 2,600 | | [Waimai](https://github.com/SophonPlus/ChineseNlpCorpus/blob/master/datasets/waimai_10k/intro.ipynb) | [C-MTEB/waimai-classification](https://modelscope.cn/datasets/C-MTEB/waimai-classification) | 外卖平台用户评论的情感分析 | 分类 | s2s | 1,000 | | [OnlineShopping](https://github.com/SophonPlus/ChineseNlpCorpus/blob/master/datasets/online_shopping_10_cats/intro.ipynb) | [C-MTEB/OnlineShopping-classification](https://modelscope.cn/datasets/C-MTEB/OnlineShopping-classification) | 在线购物网站用户评论的情感分析 | 分类 | s2s | 1,000 | | [MultilingualSentiment](https://github.com/tyqiangz/multilingual-sentiment-datasets) | [C-MTEB/MultilingualSentiment-classification](https://modelscope.cn/datasets/C-MTEB/MultilingualSentiment-classification) | 一组按三类分组的多语言情感数据集--正面、中立、负面 | 分类 | s2s | 3,000 | | [JDReview](https://huggingface.co/datasets/kuroneko5943/jd21) | [C-MTEB/JDReview-classification](https://modelscope.cn/datasets/C-MTEB/JDReview-classification) | iPhone的评论 | 分类 | s2s | 533 | 对于检索任务，从整个语料库中抽样100,000个候选项（包括真实值），以降低推理成本。 ```{seealso} - [CMTEB支持的数据集](https://github.com/FlagOpen/FlagEmbedding/blob/master/research/C_MTEB/README.md) - [MTEB支持的数据集](https://github.com/embeddings-benchmark/mteb/blob/main/docs/tasks.md) ``` ## 环境准备 安装依赖包 ```bash pip install evalscope[rag] -U ``` ## 配置评测参数 框架支持两种评测方式：单阶段评测 和 两阶段评测： - 单阶段评测：直接使用模型预测，并计算指标，支持embedding模型的检索、重排序、分类等任务。 - 两阶段评测：使用模型检索，再使用模型进行重排序，并计算指标，支持reranking模型。 ### 单阶段评测 配置文件示例如下： ```python one_stage_task_cfg = { "work_dir": "outputs", "eval_backend": "RAGEval", "eval_config": { "tool": "MTEB", "model": [ { "model_name_or_path": "AI-ModelScope/m3e-base", "pooling_mode": None, "max_seq_length": 512, "prompt": "", "model_kwargs": {"torch_dtype": "auto"}, "encode_kwargs": { "batch_size": 128, }, } ], "eval": { "tasks": [ "TNews", "CLSClusteringS2S", "T2Reranking", "T2Retrieval", "ATEC", ], "verbosity": 2, "overwrite_results": True, "top_k": 10, "limits": 500, }, }, } ``` ### API模型服务评测 使用远程API模型服务时，配置文件示例如下： ```python from evalscope import TaskConfig task_cfg = TaskConfig( eval_backend='RAGEval', eval_config={ 'tool': 'MTEB', 'model': [ { 'model_name': 'text-embedding-v3', 'api_base': 'https://dashscope.aliyuncs.com/compatible-mode/v1', 'api_key': env.get('DASHSCOPE_API_KEY', 'EMPTY'), 'dimensions': 1024, 'encode_kwargs': { 'batch_size': 10, }, } ], 'eval': { 'tasks': [ 'T2Retrieval', ], 'verbosity': 2, 'overwrite_results': True, 'limits': 30, }, }, ) ``` ### 两阶段评测 评测reranker需要用retrieval数据集，先用embedding模型检索topk，再进行排序。配置文件示例如下： ```python two_stage_task_cfg = { "work_dir": "outputs", "eval_backend": "RAGEval", "eval_config": { "tool": "MTEB", "model": [ { "model_name_or_path": "AI-ModelScope/m3e-base", "is_cross_encoder": False, "max_seq_length": 512, "model_kwargs": {"torch_dtype": "auto"}, "encode_kwargs": { "batch_size": 64, }, }, { "model_name_or_path": "OpenBMB/MiniCPM-Reranker", "is_cross_encoder": True, "max_seq_length": 512, "prompt": "为这个问题生成一个检索用的表示", "model_kwargs": {"torch_dtype": "auto"}, "encode_kwargs": { "batch_size": 32, }, }, ], "eval": { "tasks": ["T2Retrieval"], "verbosity": 2, "overwrite_results": True, "top_k": 5, "limits": 100, }, }, } ``` ## 参数说明 - `eval_backend`：默认值为 `RAGEval`，表示使用 RAGEval 评测后端。 - `eval_config`：字典，包含以下字段： - `tool`：评测工具，使用 `MTEB`。 - `model`： 模型配置列表，**单阶段评测时只能放置一个模型；两阶段评测传入两个模型，第一个模型用于检索，第二个模型用于reranking**，包含以下字段： - **对于本地加载的模型支持**： - `model_name_or_path`: `str` 模型名称或路径，支持从modelscope仓库自动下载模型。 - `is_cross_encoder`: `bool` 模型是否为交叉编码器，默认为 False；reranking模型需设置为`True`。 - `pooling_mode`: `Optional[str]` 池化模式，默认为`mean`，可选值为：“cls”、“lasttoken”、“max”、“mean”、“mean_sqrt_len_tokens”或“weightedmean”。`bge`系列模型请设置为“cls”。 - `max_seq_length`: `int` 最大序列长度，默认为 512。 - `prompt`: `str` 用于检索任务在模型前的提示，默认为空字符串。 - `model_kwargs`: `dict` 模型的关键字参数，默认值为 `{"torch_dtype": "auto"}`。 - `config_kwargs`: `Dict[str, Any]` 配置的关键字参数，默认为空字典。 - `encode_kwargs`: `dict` 编码的关键字参数，默认值为： ```python { "show_progress_bar": True, "batch_size": 32 } ``` - `hub`: `str` 模型来源，可以是 "modelscope" 或 "huggingface"。 - **对于远程API模型服务支持**： - `model_name`: `str` 模型名称。 - `api_base`: `str` 模型API服务地址。 - `api_key`: `str` 模型API密钥。 - `dimension`: `int` 模型输出维度。 - `encode_kwargs`: `dict` 编码的关键字参数，默认值为： ```python { "batch_size": 10 } ``` - `eval`：字典，包含以下字段： - `tasks`: `List[str]` 任务名称，参见[任务列表](#支持的数据集) - `top_k`: `int` 选取前 K 个结果，检索任务使用 - `verbosity`: `int` 详细程度，范围为 0-3 - `overwrite_results`: `bool` 是否覆盖结果，默认为 True - `limits`: `Optional[int]` 限制样本数量，默认为 None；检索任务不建议设置 - `hub`: `str` 数据集来源，可以是 "modelscope" 或 "huggingface" ## 模型评测 ```python from evalscope.run import run_task # Run task run_task(task_cfg=one_stage_task_cfg) # or # run_task(task_cfg=two_stage_task_cfg) ``` 输出结果如下： **单阶段评测**
输出： ```{code-block} json :caption: outputs/m3e-base/master/TNews.json { "dataset_revision": "317f262bf1e6126357bbe89e875451e4b0938fe4", "evaluation_time": 16.50650382041931, "kg_co2_emissions": null, "mteb_version": "1.14.15", "scores": { "validation": [ { "accuracy": 0.4744, "f1": 0.44562489526640825, "f1_weighted": 0.47540307398330806, "hf_subset": "default", "languages": [ "cmn-Hans" ], "main_score": 0.4744, "scores_per_experiment": [ { "accuracy": 0.48, "f1": 0.4536376605217497, "f1_weighted": 0.47800277926811163 }, { "accuracy": 0.48, "f1": 0.44713633954639176, "f1_weighted": 0.4826984434763292 }, { "accuracy": 0.462, "f1": 0.433365706955334, "f1_weighted": 0.4640970055245127 }, { "accuracy": 0.484, "f1": 0.4586732839614161, "f1_weighted": 0.4857359110392786 }, { "accuracy": 0.462, "f1": 0.4293797541165097, "f1_weighted": 0.4632657330831137 }, { "accuracy": 0.474, "f1": 0.44775120246296396, "f1_weighted": 0.4737182842092953 }, { "accuracy": 0.47, "f1": 0.4431197566080463, "f1_weighted": 0.4714830140231783 }, { "accuracy": 0.472, "f1": 0.44322381694059326, "f1_weighted": 0.47100005556357255 }, { "accuracy": 0.484, "f1": 0.45454749692062835, "f1_weighted": 0.4856239367465818 }, { "accuracy": 0.476, "f1": 0.44541393463044954, "f1_weighted": 0.47840557689910646 } ] } ] }, "task_name": "TNews" } ```

**两阶段评测**
阶段一 ```{code-block} json :caption: outputs/stage1/m3e-base/v1/T2Retrieval.json { "dataset_revision": "8731a845f1bf500a4f111cf1070785c793d10e64", "evaluation_time": 599.5170171260834, "kg_co2_emissions": null, "mteb_version": "1.14.15", "scores": { "dev": [ { "hf_subset": "default", "languages": [ "cmn-Hans" ], "main_score": 0.73143, "map_at_1": 0.22347, "map_at_10": 0.63237, "map_at_100": 0.67533, "map_at_1000": 0.67651, "map_at_20": 0.66282, "map_at_3": 0.43874, "map_at_5": 0.54049, "mrr_at_1": 0.7898912852884447, "mrr_at_10": 0.8402654617870331, "mrr_at_100": 0.8421827758769684, "mrr_at_1000": 0.8422583001072272, "mrr_at_20": 0.8415411456315557, "mrr_at_3": 0.8307469752761716, "mrr_at_5": 0.8368029984218875, "nauc_map_at_1000_diff1": 0.17749400860890877, "nauc_map_at_1000_max": 0.42844516520725967, "nauc_map_at_1000_std": 0.18789871694419072, "nauc_map_at_100_diff1": 0.17747467084779375, "nauc_map_at_100_max": 0.42732291785494575, "nauc_map_at_100_std": 0.18694287087286737, "nauc_map_at_10_diff1": 0.19976199493034202, "nauc_map_at_10_max": 0.3374436217668296, "nauc_map_at_10_std": 0.07951451707732717, "nauc_map_at_1_diff1": 0.41727578149080663, "nauc_map_at_1_max": -0.1402656422184478, "nauc_map_at_1_std": -0.26168722519030313, "nauc_map_at_20_diff1": 0.1811898211371171, "nauc_map_at_20_max": 0.40563441466210043, "nauc_map_at_20_std": 0.15927727170010608, "nauc_map_at_3_diff1": 0.31255422845809033, "nauc_map_at_3_max": 0.007523677231905161, "nauc_map_at_3_std": -0.19578481884353466, "nauc_map_at_5_diff1": 0.26073699217160473, "nauc_map_at_5_max": 0.14665611579604088, "nauc_map_at_5_std": -0.09600383298672226, "nauc_mrr_at_1000_diff1": 0.3819666309367981, "nauc_mrr_at_1000_max": 0.6285393024619401, "nauc_mrr_at_1000_std": 0.3294970299417527, "nauc_mrr_at_100_diff1": 0.3819436006743644, "nauc_mrr_at_100_max": 0.6286346262471935, "nauc_mrr_at_100_std": 0.32963045935037844, "nauc_mrr_at_10_diff1": 0.3819124721154632, "nauc_mrr_at_10_max": 0.6292778905762176, "nauc_mrr_at_10_std": 0.3298187966196067, "nauc_mrr_at_1_diff1": 0.3862589251033909, "nauc_mrr_at_1_max": 0.589976680174432, "nauc_mrr_at_1_std": 0.2780515387897469, "nauc_mrr_at_20_diff1": 0.38198959771391816, "nauc_mrr_at_20_max": 0.6290569436652999, "nauc_mrr_at_20_std": 0.3301570340189363, "nauc_mrr_at_3_diff1": 0.3825046940733129, "nauc_mrr_at_3_max": 0.6282507269128365, "nauc_mrr_at_3_std": 0.3260807934869131, "nauc_mrr_at_5_diff1": 0.3816317396711923, "nauc_mrr_at_5_max": 0.6288655177904692, "nauc_mrr_at_5_std": 0.3298854062538469, "nauc_ndcg_at_1000_diff1": 0.21319598381916555, "nauc_ndcg_at_1000_max": 0.5328295949130256, "nauc_ndcg_at_1000_std": 0.2946773445135694, "nauc_ndcg_at_100_diff1": 0.2089807772703975, "nauc_ndcg_at_100_max": 0.5239397690321543, "nauc_ndcg_at_100_std": 0.29123456982125717, "nauc_ndcg_at_10_diff1": 0.20555333230027603, "nauc_ndcg_at_10_max": 0.44316027023003046, "nauc_ndcg_at_10_std": 0.1921835220940756, "nauc_ndcg_at_1_diff1": 0.3862589251033909, "nauc_ndcg_at_1_max": 0.589976680174432, "nauc_ndcg_at_1_std": 0.2780515387897469, "nauc_ndcg_at_20_diff1": 0.20754208582741446, "nauc_ndcg_at_20_max": 0.4786092392092643, "nauc_ndcg_at_20_std": 0.23536973680564616, "nauc_ndcg_at_3_diff1": 0.1902823773882388, "nauc_ndcg_at_3_max": 0.5400466380622567, "nauc_ndcg_at_3_std": 0.2713874990424778, "nauc_ndcg_at_5_diff1": 0.18279298790691637, "nauc_ndcg_at_5_max": 0.4916119327522918, "nauc_ndcg_at_5_std": 0.2375397192963552, "nauc_precision_at_1000_diff1": -0.20510380600112582, "nauc_precision_at_1000_max": 0.4958820760698651, "nauc_precision_at_1000_std": 0.5402465580496146, "nauc_precision_at_100_diff1": -0.1994322347949809, "nauc_precision_at_100_max": 0.5206762748551254, "nauc_precision_at_100_std": 0.5568154081333078, "nauc_precision_at_10_diff1": -0.16707155441197413, "nauc_precision_at_10_max": 0.5600612846655972, "nauc_precision_at_10_std": 0.49419688804691536, "nauc_precision_at_1_diff1": 0.3862589251033909, "nauc_precision_at_1_max": 0.589976680174432, "nauc_precision_at_1_std": 0.2780515387897469, "nauc_precision_at_20_diff1": -0.18471041949530417, "nauc_precision_at_20_max": 0.5458950955439645, "nauc_precision_at_20_std": 0.5355982267058214, "nauc_precision_at_3_diff1": -0.03826790088047189, "nauc_precision_at_3_max": 0.5833083970750171, "nauc_precision_at_3_std": 0.380196662597275, "nauc_precision_at_5_diff1": -0.11789367842600275, "nauc_precision_at_5_max": 0.5708494593335263, "nauc_precision_at_5_std": 0.42860609671688105, "nauc_recall_at_1000_diff1": 0.1341309660059583, "nauc_recall_at_1000_max": 0.5923755841077135, "nauc_recall_at_1000_std": 0.5980459502693942, "nauc_recall_at_100_diff1": 0.12181394285840096, "nauc_recall_at_100_max": 0.47090136790318127, "nauc_recall_at_100_std": 0.3959369184297595, "nauc_recall_at_10_diff1": 0.17356300971546512, "nauc_recall_at_10_max": 0.25475707245853674, "nauc_recall_at_10_std": 0.041819982320384745, "nauc_recall_at_1_diff1": 0.41727578149080663, "nauc_recall_at_1_max": -0.1402656422184478, "nauc_recall_at_1_std": -0.26168722519030313, "nauc_recall_at_20_diff1": 0.14273713155999543, "nauc_recall_at_20_max": 0.36251116771924663, "nauc_recall_at_20_std": 0.1912123941692314, "nauc_recall_at_3_diff1": 0.2873719855400218, "nauc_recall_at_3_max": -0.041198403561830285, "nauc_recall_at_3_std": -0.21921947922872737, "nauc_recall_at_5_diff1": 0.23680082643694844, "nauc_recall_at_5_max": 0.06580524171324151, "nauc_recall_at_5_std": -0.14104561361502632, "ndcg_at_1": 0.78989, "ndcg_at_10": 0.73143, "ndcg_at_100": 0.78829, "ndcg_at_1000": 0.80026, "ndcg_at_20": 0.75787, "ndcg_at_3": 0.7417, "ndcg_at_5": 0.72641, "precision_at_1": 0.78989, "precision_at_10": 0.37304, "precision_at_100": 0.04828, "precision_at_1000": 0.00511, "precision_at_20": 0.21403, "precision_at_3": 0.65461, "precision_at_5": 0.54942, "recall_at_1": 0.22347, "recall_at_10": 0.73318, "recall_at_100": 0.91093, "recall_at_1000": 0.97197, "recall_at_20": 0.81286, "recall_at_3": 0.46573, "recall_at_5": 0.59383 } ] }, "task_name": "T2Retrieval" } ```
阶段二 ```{code-block} json :caption: outputs/stage2/jina-reranker-v2-base-multilingual/master/T2Retrieval.json { "dataset_revision": "8731a845f1bf500a4f111cf1070785c793d10e64", "evaluation_time": 332.15709686279297, "kg_co2_emissions": null, "mteb_version": "1.14.15", "scores": { "dev": [ { "hf_subset": "default", "languages": [ "cmn-Hans" ], "main_score": 0.661, "map_at_1": 0.24264, "map_at_10": 0.56291, "map_at_100": 0.56291, "map_at_1000": 0.56291, "map_at_20": 0.56291, "map_at_3": 0.4714, "map_at_5": 0.56291, "mrr_at_1": 0.841969139049623, "mrr_at_10": 0.8689147524694633, "mrr_at_100": 0.8689147524694633, "mrr_at_1000": 0.8689147524694633, "mrr_at_20": 0.8689147524694633, "mrr_at_3": 0.8664883979192248, "mrr_at_5": 0.8689147524694633, "nauc_map_at_1000_diff1": 0.12071580301051653, "nauc_map_at_1000_max": 0.2536691069727338, "nauc_map_at_1000_std": 0.343624832364704, "nauc_map_at_100_diff1": 0.12071580301051653, "nauc_map_at_100_max": 0.2536691069727338, "nauc_map_at_100_std": 0.343624832364704, "nauc_map_at_10_diff1": 0.12071580301051653, "nauc_map_at_10_max": 0.2536691069727338, "nauc_map_at_10_std": 0.343624832364704, "nauc_map_at_1_diff1": 0.47964980727810325, "nauc_map_at_1_max": -0.08015044571696166, "nauc_map_at_1_std": 0.3507257834956417, "nauc_map_at_20_diff1": 0.12071580301051653, "nauc_map_at_20_max": 0.2536691069727338, "nauc_map_at_20_std": 0.343624832364704, "nauc_map_at_3_diff1": 0.23481937699306626, "nauc_map_at_3_max": 0.10372745264123306, "nauc_map_at_3_std": 0.45345158923063256, "nauc_map_at_5_diff1": 0.12071580301051653, "nauc_map_at_5_max": 0.2536691069727338, "nauc_map_at_5_std": 0.343624832364704, "nauc_mrr_at_1000_diff1": 0.23393918304502795, "nauc_mrr_at_1000_max": 0.8703379129725659, "nauc_mrr_at_1000_std": 0.5785333616122065, "nauc_mrr_at_100_diff1": 0.23393918304502795, "nauc_mrr_at_100_max": 0.8703379129725659, "nauc_mrr_at_100_std": 0.5785333616122065, "nauc_mrr_at_10_diff1": 0.23393918304502795, "nauc_mrr_at_10_max": 0.8703379129725659, "nauc_mrr_at_10_std": 0.5785333616122065, "nauc_mrr_at_1_diff1": 0.2520016067648708, "nauc_mrr_at_1_max": 0.8560897633767299, "nauc_mrr_at_1_std": 0.5642467684745208, "nauc_mrr_at_20_diff1": 0.23393918304502795, "nauc_mrr_at_20_max": 0.8703379129725659, "nauc_mrr_at_20_std": 0.5785333616122065, "nauc_mrr_at_3_diff1": 0.2343988881957151, "nauc_mrr_at_3_max": 0.8695482778251757, "nauc_mrr_at_3_std": 0.5799167198804328, "nauc_mrr_at_5_diff1": 0.23393918304502795, "nauc_mrr_at_5_max": 0.8703379129725659, "nauc_mrr_at_5_std": 0.5785333616122065, "nauc_ndcg_at_1000_diff1": 0.11252208055013257, "nauc_ndcg_at_1000_max": 0.3417865079349515, "nauc_ndcg_at_1000_std": 0.3623961771041499, "nauc_ndcg_at_100_diff1": 0.11252208055013257, "nauc_ndcg_at_100_max": 0.3417865079349515, "nauc_ndcg_at_100_std": 0.3623961771041499, "nauc_ndcg_at_10_diff1": 0.10015448775533999, "nauc_ndcg_at_10_max": 0.3761759074862075, "nauc_ndcg_at_10_std": 0.35152523471339914, "nauc_ndcg_at_1_diff1": 0.2524564785684737, "nauc_ndcg_at_1_max": 0.8566368743831702, "nauc_ndcg_at_1_std": 0.5635391925059349, "nauc_ndcg_at_20_diff1": 0.11228113618796766, "nauc_ndcg_at_20_max": 0.34274993051851965, "nauc_ndcg_at_20_std": 0.36216437469674284, "nauc_ndcg_at_3_diff1": -0.062134030685870506, "nauc_ndcg_at_3_max": 0.7183183844837573, "nauc_ndcg_at_3_std": 0.3352626268658533, "nauc_ndcg_at_5_diff1": -0.04476981761624879, "nauc_ndcg_at_5_max": 0.6272060974309411, "nauc_ndcg_at_5_std": 0.21341258393783158, "nauc_precision_at_1000_diff1": -0.3554940965683014, "nauc_precision_at_1000_max": 0.605443274008298, "nauc_precision_at_1000_std": -0.13073611213585504, "nauc_precision_at_100_diff1": -0.35549409656830133, "nauc_precision_at_100_max": 0.6054432740082977, "nauc_precision_at_100_std": -0.13073611213585531, "nauc_precision_at_10_diff1": -0.3554940965683011, "nauc_precision_at_10_max": 0.6054432740082981, "nauc_precision_at_10_std": -0.1307361121358551, "nauc_precision_at_1_diff1": 0.2524564785684737, "nauc_precision_at_1_max": 0.8566368743831702, "nauc_precision_at_1_std": 0.5635391925059349, "nauc_precision_at_20_diff1": -0.3554940965683011, "nauc_precision_at_20_max": 0.6054432740082981, "nauc_precision_at_20_std": -0.1307361121358551, "nauc_precision_at_3_diff1": -0.3377658698816377, "nauc_precision_at_3_max": 0.6780151277792397, "nauc_precision_at_3_std": 0.12291559606586676, "nauc_precision_at_5_diff1": -0.3554940965683011, "nauc_precision_at_5_max": 0.6054432740082981, "nauc_precision_at_5_std": -0.1307361121358551, "nauc_recall_at_1000_diff1": 0.1091970342988605, "nauc_recall_at_1000_max": 0.18339955163544436, "nauc_recall_at_1000_std": 0.30756376767627086, "nauc_recall_at_100_diff1": 0.1091970342988605, "nauc_recall_at_100_max": 0.18339955163544436, "nauc_recall_at_100_std": 0.30756376767627086, "nauc_recall_at_10_diff1": 0.1091970342988605, "nauc_recall_at_10_max": 0.18339955163544436, "nauc_recall_at_10_std": 0.30756376767627086, "nauc_recall_at_1_diff1": 0.47964980727810325, "nauc_recall_at_1_max": -0.08015044571696166, "nauc_recall_at_1_std": 0.3507257834956417, "nauc_recall_at_20_diff1": 0.1091970342988605, "nauc_recall_at_20_max": 0.18339955163544436, "nauc_recall_at_20_std": 0.30756376767627086, "nauc_recall_at_3_diff1": 0.22013063499116758, "nauc_recall_at_3_max": 0.054749114965246065, "nauc_recall_at_3_std": 0.4258163949018153, "nauc_recall_at_5_diff1": 0.1091970342988605, "nauc_recall_at_5_max": 0.18339955163544436, "nauc_recall_at_5_std": 0.30756376767627086, "ndcg_at_1": 0.84188, "ndcg_at_10": 0.661, "ndcg_at_100": 0.6534, "ndcg_at_1000": 0.6534, "ndcg_at_20": 0.65358, "ndcg_at_3": 0.7826, "ndcg_at_5": 0.74517, "precision_at_1": 0.84188, "precision_at_10": 0.27471, "precision_at_100": 0.02747, "precision_at_1000": 0.00275, "precision_at_20": 0.13736, "precision_at_3": 0.68633, "precision_at_5": 0.54942, "recall_at_1": 0.24264, "recall_at_10": 0.59383, "recall_at_100": 0.59383, "recall_at_1000": 0.59383, "recall_at_20": 0.59383, "recall_at_3": 0.48934, "recall_at_5": 0.59383 } ] }, "task_name": "T2Retrieval" } ```
## 自定义评测数据集 ```{seealso} [自定义检索评测](../../../advanced_guides/custom_dataset/embedding.md) ```